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Extracting Bounded-level Modules from Deductive RDF Triplestores

Marie-Christine Rousset 1, 2, 3 Federico Ulliana 4 
4 GRAPHIK - Graphs for Inferences on Knowledge
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : We present a novel semantics for extracting bounded-level modules from RDF ontologies and databases augmented with safe inference rules, a la Datalog. Dealing with a recursive rule language poses challenging issues for defining the module semantics, and also makes module extraction algorithmically unsolvable in some cases. Our results include a set of module extraction algorithms compliant with the novel semantics. Experimental results show that the resulting framework is effective in extracting expressive modules from RDF datasets with formal guarantees, whilst controlling their succinctness.
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Submitted on : Thursday, January 15, 2015 - 10:02:37 AM
Last modification on : Friday, August 5, 2022 - 3:03:00 PM
Long-term archiving on: : Friday, September 11, 2015 - 6:49:06 AM


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  • HAL Id : lirmm-01086951, version 1
  • PRODINRA : 313444


Marie-Christine Rousset, Federico Ulliana. Extracting Bounded-level Modules from Deductive RDF Triplestores. AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI). Austin, USA., Jan 2015, Austin, TX, United States. ⟨lirmm-01086951⟩



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